Abstract

The sound of crashing waves, the roar of fast-moving cars – sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.

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Acknowledgments

This work was supported by NSF grants #1524817 to A.T; NSF grants #1447476 and #1212849 to W.F.; a McDonnell Scholar Award to J.H.M.; and a Microsoft Ph.D. Fellowship to A.O. It was also supported by Shell Research, and by a donation of GPUs from NVIDIA. We thank Phillip Isola and Carl Vondrick for the helpful discussions, and the anonymous reviewers for their comments (in particular, for suggesting the comparison with texton features in Sect. 4.2).